Research

Bridge gaps among Geology, Geophysics, & Computer Science

Image processing, Machine learning, Seismic interpretation, Subsurface modeling, Geophysical inversion, Computer graphics….


 


Note: we compute and display all the results using our freely available Java packages (etc., Mines JTK).


 

FaultNet3D: predicting fault probabilities, strikes and dips with a common CNN

The 3D anisotropic ellipsoids in (a) and (b) are colored by fault probabilities and oriented by fault strikes and dips, which are simultaneously estimated by using a CNN model. These ellipsoids are further stacked to compute final fault images of probabilities (c), strikes and dips.

With a CNN model, trained by only synthetic datasets, we are able to simultaneously estimate three images of fault probabilities (a), dips (b), and strikes (c). From these three fault images, we further construct fault cells (d) that are colored by fault probabilities and oriented by fault strikes and dips. These fault cells are further linked to form fault skins/surfaces that are colored, respectively, by fault probabilities (e) and strikes (f).

[more results]

Click the above figure to view high-resolution images:)

Collaborators:

Yunzhi Shi and Sergey Fomel at UT Austin, TX, USA

Luming Liang at Uber, CO, USA

Qie Zhang and Anar Z. Yusifov at BP American Inc., TX, USA

Journal paper is in submission.

SEG abstract accepted:

 Wu, X., Y. Shi, S. Fomel, and L. Liang, Convolutional neural network for fault interpretation in seismic images. 88th SEG, Expanded Abstracts. [PDF]

This work will be presented at SEG 2018.

(I gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research!)



FaultSeg3D: using synthetic datasets to train an end-to-end CNN for 3D fault segmentation

Click the above figure to view high-resolution images:)

More tests on SEG open datasets:

Kerry-3D

Opunake-3D, …

The training datasets, codes, and trained model will be available soon.

Collaborators:

Yunzhi Shi and Sergey Fomel at UT Austin, TX, USA

Luming Liang at Uber, CO, USA

Coming soon…  This work will be presented at TCCS fall sponsor meeting, 2018.

 


Toward accurate seismic flattening

A input seismic image (a) with complicated structures, conventional flattening with slopes (b), improved flattening (c) with the proposed accurate flattening method, and horizons (d) computed by the improved flattening. This method works well in 3D as in (e), (f) and (g). No fault information is used as constraints in these examples. Click the above figure to view high-resolution images.

Coming soon…  This work will be presented at TCCS fall sponsor meeting, 2018.


]Normal fault populations in the Costa Rica Margin (NSF project, collaborate with Dr. Nathan Bangs)

From the 3D seismic image (right above) acquired in Costa Rica subduction area (left above), we automatically compute  more than 30 thousands 3D fault surfaces and their strikes, dips, and slips. With these computed high-resolution fault strikes, we are able to make the “blooming roses” (the cartoon below) to visualize the fault-strike variations with depth (or geologic time) and space.


 

Automatic fault interpretation with optimal surface voting

From an input image (a) of seismic discontinuity (1-planarity) attribute, we first compute a voting score map (b), from which fault surfaces (colored by fault strikes in (c)) are then automatically extracted.

Wu, X. and S. Fomel, 2018, Automatic fault interpretation with optimal surface voting. Geophysics, Vol. 83(5), O67-O82. [PDF] [CODE]

This work will be presented at SEG 2018.


Least-squares horizons with local slopes and multi-grid correlations

From a 3D seismic image (a), two horizon surfaces (colored by seismic amplitude) are automatically computed with one (b) and two (c) control points (colored by green in (b) and (c)).

Wu, X. and S. Fomel, 2018, Least-squares horizons with local slopes and multi-grid correlations, Geophysics, Vol. 83(4), IM29-IM40. [PDF] [CODE]

This work will be presented at SEG 2018.


Fast salt boundary interpretation with optimal path picking

Salt boundary interpretation is a crucial step for velocity-model building in seismic migration, but re- mains a big challenge for automatic methods and a highly labor-intensive task for manual interpretation. We propose a semi-automatic method to efficiently and accurately extract 2D and 3D complicated salt boundaries from seismic envelope images. In 2D salt boundary extraction, we first pick a few points to interpolate an initial curve that is close to the true salt boundary. These points are picked near the salt boundary but are not required to be exactly on the boundary, which makes human interactions convenient and efficient. We then resample the envelope image in a band area centered at the initial curve to obtain a new image where the true salt boundary is an open curve extending from left to right. We then extract the salt boundary in the new image using an optimal-path picking algorithm, which is robust to track a highly discontinuous salt boundary by picking the optimal path with globally maximum envelope values. We finally map the picked path back to the original image to obtain a final salt boundary. In 3D salt boundary extraction, we apply the 2D method to recursively pick 2D salt boundaries in a sequence of inline or crossline slices and then fit these 2D boundaries to obtain a 3D surface of the salt boundary. In this proposed recursive picking, human interactions are greatly reduced by using a salt boundary picked in the previous slice as an initial curve for picking in a followed slice.
Wu, X., S. Fomel, and M. Hudec, 2017, Fast salt boundary interpretation with optimal path picking. Geophysics,
Vol. 83(3), O45-O53. [PDF]  [CODE]

Incremental correlation of multiple well logs following geologically optimal neighbors

Well-log correlation is a crucial step to construct cross sections in estimating structures between wells and building subsurface models. Manually correlating multiple logs can be highly subjective and labor-intensive. We propose a weighted incremental correlation method to efficiently correlate multiple well logs following a geologically optimal path. In this method, we first automatically compute an optimal path that starts with longer logs and follows geologically more continuous structures. We then use the dynamic warping technique to sequentially correlate the logs following the path. To avoid potential error propagation with the path, we modify the dynamic warping algorithm to use all the previously correlated logs as references to correlate the current log in the path. During the sequential correlations, we compute geologic distances between the current log and all the reference logs. Such distances are proportional to Euclidean distances but increase dramatically across discontinuous structures such as faults and unconformities that separate the current log from the reference logs. We also compute correlation confidences to provide quantitatively quality control of the correlation results. We use both the geologic distances and correlation confidences to weight the references in correlating the current log. By using this weighted incremental correlation method, each log is optimally correlated to all the logs that are geologically closer and are ordered with higher priorities in the path. Hundreds of well logs from the Teapot Dome survey demonstrate the efficiency and robustness of the method.

 

Wu, X., Y. Shi, S. Fomel, and F. Li, 2018, Incremental correlation of multiple well logs following geologically optimal neighbors. Interpretation, Vol. 6(3), T713-T722. [PDF]


Directional structure tensors in estimating seismic structural and stratigraphic orientations

From a 3D seismic image (a), orientations (red segments in (b)) of channels are estimated without picking the horizon surface.

Conventional structure-tensor method often generates significant errors in estimating orientations of the reflections with steep and rapidly varying slopes. To better estimate reflection orientations, we propose to construct structure tensors in a new space, where the reflections are mostly flat or only slightly dipping and the variation of reflection slopes is reduced. We use these constructed structure tensors to compute reflection normals in this new space and then transform the normals back to obtain a better estimation of reflection orientations in the original space. Seismic stratigraphic features such as channels are often aligned within dipping reflections. It is not discussed previously by others to estimate orientations of such features directly from a seismic image. An ideal way to estimate stratigraphic orientations is to first extract a horizon surface with stratigraphic features, and then construct structure tensors with gradients on the surface to estimate the orientations of the features. However, extracting horizon surfaces can be a difficult and time-consuming task in practice. Fortunately, computing gradients on a horizon surface is only a local operation and is equivalent to directly compute directional derivatives along reflection slopes without picking horizons. Based on this observation, we propose to use an equivalent but more efficient way to estimate seismic stratigraphic orientations by using structure tensors constructed with the directional derivatives along reflections.

Wu, X. and X., Janson, 2017, Directional structure tensors in estimating seismic structural and stratigraphic orientations. Geophysical Journal International, Vol. 210(1), 534-548. [PDF]


Efficient structure- and stratigraphy-oriented smoothing to simultaneously enhance reflections, faults and channels


wu16sss-001

From an input 3D seismic image (a), I simultaneously compute a smoothed seismic image (b) with enhanced reflections and channels and a channel image (c) with enhanced channel features.

In this paper, I propose methods to enhance seismic reflections, faults, and channels and simultaneously obtain mappings of faults and channels. In the methods, I first estimate orientations of reflections, faults, and channels directly from a seismic image. I then use the estimated orientations to control smoothing directions in an efficient iterative diffusion scheme to smooth a seismic image along reflections and channels. In this iterative scheme, I also efficiently compute mappings of faults and channels, which are used to control smoothing extents in the diffusion to stop smoothing across faults and channels. This diffusion scheme iteratively smoothes a seismic image along reflections and channels while at the same time updating the mappings of faults and channels. After a small number of diffusion steps, I finally obtain enhanced mappings of faults and channels and a smoothed seismic image with enhanced reflections, faults, and channels.

Wu, X., 2016, Efficient structure- and stratigraphy-oriented smoothing to simultaneously enhance seismic reflections, faults, and channels. Geophysical prospecting, submitted.


 

Structure-, stratigraphy-, and fault-guided regularization in geophysical inversion


a) A 2D migrated seismic image , and b) corresponding picked migration velocity . c) Interval velocity estimated from the migration velocity (b) with shaping regularization. d) Interval velocity estimated from the migration velocity (b) with seismic structure- and fault-guided regularization.

Geophysical inversion is often ill-posed because of inaccurate and insufficient data. Regularization is often applied to the inversion problem to obtain a stable solution by imposing additional constraints on the model. Common regularization schemes impose isotropic smoothness on solutions and may have difficulties in obtaining geologically reasonable models that are often supposed to be anisotropic and conform to subsurface structural and stratigraphic features. I introduce a general method to incorporate constraints of seismic structural and stratigraphic orientations and fault slips into geophysical inversion problems. I first use a migrated seismic image to estimate structural and stratigraphic orientations and fault slip vectors that correlate fault blocks on opposite sides of a fault. I then use the estimated orientations and fault slips to construct simple and convenient anisotropic regularization operators in inversion problems to spread information along structural and stratigraphic orientations and across faults. In this way, we are able to compute inverted models that conform to seismic reflectors, faults, and stratigraphic features such as channels. The regularization is also helpful to integrate well-log properties into the inversion by spreading the measured rock properties away from the well-log positions into the whole inverted model across faults and along structural and stratigraphic orientations. I use a 3D synthetic example of impedance inversion to illustrate the structure-, stratigraphy-, and fault-guided regularization method. I further applied the method to estimate seismic interval velocity and to compute structure- and stratigraphy-oriented semblance.

Wu, X. , 2017, Structure-, stratigraphy-, and fault-guided regularization in geophysical inversion. Geophysical Journal International, Vol. 210(1), 184-195. [PDF]


Improved horizon extraction and seismic attributes for seismic geomorphology analysis of carbonate systems



Directional structure-tensor based coherence to detect seismic channels and faults


Conventional (a) and directional (b) structure-tensor based coherence.

A coherence image can be computed from the eigenvalues of conventional structure tenors, which are outer products of gradients of a seismic image. I propose a simple but effective method to improve such a coherence image by using directional structure tensors, which are different from the conventional structure tensors in only two aspects. Firstly, instead of using image gradients with vertical and horizontal derivatives, I use directional derivatives, computed in directions perpendicular and parallel to seismic structures (reflectors), to construct directional structure tensors. With these directional derivatives, lateral seismic discontinuities, especially those subtle stratigraphic features aligned within dipping structures, can be better captured in the structure tensors. Secondly, instead of applying Gaussian smoothing to each element of the constructed structure tensors, I apply approximately fault- and stratigraphy-oriented smoothing to enhance the lateral discontinuities corresponding to faults and stratigraphic features in the structure tensors.

Wu, X., 2016, Directional structure-tensor based coherence to detect seismic channels and faults. Geophysics82(2), A13-A17. [PDF]


 

Methods to enhance seismic faults and construct fault surfaces


A fault attribute image before (a) and after (b) enhancement.

We propose a method to enhance a precomputed fault attribute image, and simultaneously estimate fault strikes and dips. In this enhanced image, image features are smoothed along fault orientations so that the features unrelated to faults are suppressed while those fault features are more continuous and prominent. We then compute fault samples on the ridges of an enhanced fault attribute image. Each fault sample corresponds to one and only one seismic image sample and is oriented by the estimated fault strike and dip. Fault surfaces can be constructed by directly linking the oriented fault samples with consistent fault strikes and dips. For complicated cases with missing fault samples and noisy samples, we further propose to use a perceptual grouping method to infer fault surfaces that reasonably fit the positions and orientations of the fault samples. We apply these methods to 3D synthetic and real examples and successfully extract multiple intersecting fault surfaces and complete fault surfaces without holes.

Wu, X. and Z. Zhu 2017, Methods to enhance seismic faults and construct fault surfaces. Computer & Geosciences, Vol. 107, 37-48.


 Building 3D subsurface models conform to seismic horizons, faults, unconformities, and stratigraphic features 


wu16Psm.001

A 3D density model is first interpolated form the density logs in the flattened space (a), and then is mapped back into the original space to obtain a subsurface model (b) that conforms to seismic horizons, faults, unconformities, and stratigraphic features (channel).

I propose an automatic method to fully use both seismic and borehole data to build subsurface models that honor borehole measurements and conform to seismic horizons, faults, unconformities, and stratigraphic features such as channels. In this method, I first automatically remove the faulting and folding in both seismic and borehole data and map them into a flattened space, in which seismic reflectors and borehole measurements corresponding to the same geologic layers are horizontally aligned. I then build a subsurface model in this flattened space by computing a sequence of 2D horizontal interpolations of well logs. Each horizontal interpolation is guided by the stratigraphic features apparent in the corresponding horizontal seismic slice, so that the interpolant conforms to the seismic stratigraphic features. I finally map the interpolated model back into the input space and obtain a subsurface model that honors both the seismic and borehole data.

Wu, X., 2016, 3D seismic image processing for subsurface modeling. Geophysics82(3), IM21-IM30. [PDF]


 

  Methods to compute salt likelihoods and extract salt boundaries from 3D seismic images 


wu16salt.001

Salt boundary surfaces are extracted as zero contours of a computed indicator function

From a 3D seismic image, I first efficiently compute a salt likelihood image, in which the ridges of likelihood values indicate locations of salt boundaries. I then extract salt samples on the ridges. These samples can be directly connected to construct salt boundaries in cases when salt structures are simple and the boundaries are clean. In more complicated cases, these samples may be noisy and incomplete, and some of the samples can be outliers unrelated to salt boundaries. Therefore, I finally develop a method to accurately fit noisy salt samples, reasonably fill gaps, and handle outliers to simultaneously construct multiple salt boundaries. In this step of constructing salt boundaries, I also propose a convenient way to incorporate human interactions to obtain more accurate salt boundaries in especially complicated cases.

Wu, X., 2016, Methods to compute salt likelihoods and extract salt boundaries from 3D seismic images. Geophysics81(6), IM119-IM126. [PDF]


 

  Simultaneous multiple well-seismic ties with flattened synthetic and real seismograms


wu16swt.001

Velocity logs before (a) and after (b) tying. Image-guided interpolation of the velocity logs after tying (b) is laterally more continuous than the one before tying (a).

Numerous methods have been proposed to compute well-seismic ties by correlating real seismograms with synthetic seismograms computed from velocity and density logs. However, most methods tie multiple wells to seismic data one-by-one, hence do not guarantee lateral consistency among multiple well ties. We propose to simultaneously tie multiple wells by first flattening synthetic and real seismograms so that all seismic reflectors are horizontally aligned. By doing this, we turn multiple well-seismic tying into a 1D correlation problem. We then simply compute only vertically-variant but laterally-constant shifts to correlate these horizontally aligned (flattened) synthetic and real seismograms. This two-step correlation method maintains lateral consistency among multiple well ties by computing a laterally and vertically optimized correlation of all synthetic and real seismograms.

Wu, X. and G. Caumon, 2017, Simultaneous multiple well-seismic ties with flattened synthetic and real seismograms. Geophysics, Vol. 82(1), IM13-IM20.. [PDF]


 

  Automatically interpreting all faults, unconformities, and horizons from 3D seismic images 


wu16fuh.001

Automatically computed faults, unconformities, and horizons

We have proposed a processing procedure to automatically extract all the faults, unconformities, and horizon surfaces from a 3D seismic image. In our processing, we first extracted fault surfaces, estimated fault slips, and undid the faulting in the seismic image. Then, we extracted unconformities from the unfaulted image with continuous reflectors across faults. Finally, we used the unconformities as constraints for image flattening and horizon extraction. Most of the processing was image processing or array processing and was achieved by efficiently solving partial differential equations.

Wu, X. and D. Hale, 2016, Automatically interpreting all faults, unconformities, and horizons from 3D seismic imagesInterpretation, 4(2), 1-11. [Link]  [PDF]


 

  Moving faults while unfaulting 3D seismic images


wu15unfault.001

A 3D seismic image before (a) and after (b) unfaulting

We developed two methods to compute vector shifts that simultaneously move fault blocks and the faults themselves to obtain an unfaulted image with minimal distortions. For both methods, we use estimated fault positions and slip vectors to construct unfaulting equations for image samples alongside faults, and we construct simple partial differential equations for samples away from faults. We solve these two different kinds of equations simultaneously to compute unfaulting vector shifts that are continuous everywhere except at faults.

Wu, X., S. Luo, and D. Hale, 2016, Moving faults while unfaulting 3D seismic images. Geophysics, 81(2), IM25-IM33. [Link] [PDF]

Wu, X., S. Luo, and D. Hale, 2016, Moving faults while unfaulting 3D seismic images. CWP Report 839. [Link]

Technical talk: https://www.youtube.com/watch?v=gDxfLuYf3C8


 

  3D seismic image processing for faults 


wu15fault.001

Linked data structure (a) is used to construct intersecting fault surfaces (b).

Numerous methods have been proposed to automatically extract fault surfaces from 3D seismic images, and those surfaces are often represented by meshes of triangles or quadrilaterals. Such mesh data structures are more complex than the arrays used to represent seismic images, and are more complex than necessary for subsequent processing tasks, such as that of automatically estimating fault slip vectors. To facilitate image processing for faults, we propose a simpler linked data structure in which each sample of a fault corresponds to exactly one image sample. Using this linked data structure, we extracted multiple intersecting fault surfaces from 3D seismic images. We then used the same structure in subsequent processing to estimate fault slip vectors, and to assess the accuracy of estimated slips by unfaulting the seismic images.

 

Wu, X. and D. Hale, 2016, 3D seismic image processing for faults. Geophysics, 81 (2), IM1-IM11. [Link] [PDF]

Wu, X. and D. Hale, 2016, 3D seismic image processing for faults. CWP Report 838. [Link]

Technical talk: https://www.youtube.com/watch?v=wp6Vhv3BxBE


 

  3D seismic image processing for unconformities 


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Unconformity likelihood (a) and unconformity surfaces (b)

We propose a 3D seismic unconformity attribute to detect complete unconformities, highlighting both their termination areas and correlative conformities. We then extract unconformity surfaces on the ridges of the unconformity attribute image. These detected unconformities are further used as constraints to more accurately estimate seismic normal vectors at unconformities. Then, using seismic normal vectors and detected unconformities as constraints, we can better flatten seismic images containing unconformities.

 

Wu, X. and D. Hale, 2015, 3D seismic image processing for unconformities. Geophysics, 80 (2), IM35-IM44. [Link] [PDF]

Wu, X. and D. Hale, 2015, 3D seismic image processing for unconformities. CWP Report 813. [Link]

Technical talk: https://www.youtube.com/watch?v=RjhtCvexHhY


  Horizon volumes with interpreted constraints


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A 3D view of 6 seismic horizons (a) with cut-away views (b) to show more details.

We propose two methods for constructing seismic horizons aligned with reflectors in a 3D seismic image. The first method extracts horizons one at a time; the second generates at once an entire volume of horizons. The most significant new aspect of both methods is the ability to specify, perhaps interactively during interpretation, a small number of control points that may be scattered through- out a 3D seismic image. Examples show that control points enable the accurate extraction of horizons from seismic images in which noise, unconformities, and faults are apparent. These points represent constraints that we implement simply as preconditioners in the conjugate gradient method used to construct horizons.

Wu, X. and D. Hale, 2015, Horizon volumes with interpreted constraints. Geophysics, 80 (2), IM21-IM33. [Link] [PDF]

Wu, X. and D. Hale, 2015, Horizon volumes with interpreted constraints. CWP Report 812[Link]

Technical talk: https://www.youtube.com/watch?v=w6wtf20OwCM


 

  Extracting horizons and sequence boundaries from 3D seismic images


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An extracted horizon is colored by amplitude (a) and travel time (b)

We first introduce a globally optimal method to efficiently extract a horizon from a seismic image. We then use scattered control points as constraints to enable our horizon-extraction method to extract sequence boundaries. Finally, we propose an active-surface method to refine the globally optimized horizons to align with amplitude peaks or troughs and thereby reveal more geologic details.

Wu, X. and D. Hale, 2013Extracting horizons and sequence boundaries from 3D seismic images.  83rd Annual Meeting of the Society of Exploration Geophysics, Expanded Abstracts. [Link]

Wu, X. and D. Hale, 2013Extracting horizons and sequence boundaries from 3D seismic images.  CWP Report 766. [Link]

Technical talk: https://www.youtube.com/watch?v=0vNoM4-c3E0


 

Generating a relative geologic time volume by improved 3D graph-cut-based phase unwrapping method with horizon and unconformity constraints


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An input 3D seismic amplitude volume (a) and the computed RGT volume (b).

We propose a robust phase unwrapping method to compute a relative geologic time volume from a 3D seismic instantaneous phase volume. We provide a convenient way to incorporate interpreted horizons and unconformities into our phase unwrapping method to obtain more reliable results in cases complicated by noise, faults, and unconformities. Using a computed RGT volume, we further automatically generate a 3D seismic Wheeler volume.

 

Wu, X. and G. Zhong, 2012, Generating a relative geologic time volume by improved 3D graph-cut-based phase unwrapping Method with horizon and unconformity constraints. Geophysics, 77 (4), O21-O34. [Link] [PDF]